Edge-Computing Enabled Real-Time Respiratory Monitoring and Breathing Pattern Detection

Detecting respiratory disorders can be improved with real-time diagnostic solutions, where monitoring respiration rates and patterns can act as early indicators for various cardiorespiratory diseases. We present a cost-effective edge-computing method utilizing wearable technology for respiratory disorder detection. Our system employs a wearable device with an IMU sensor for real-time signal transmission to a custom smart-phone app which enables thorough visualization of respiratory signals, continuous monitoring of respiration rates, and rapid alarms for breathing anomalies, alongside ECG functionalities. A novel approach is introduced for respiratory pattern detection that includes a pre-trained AI model for apnea detection and classification of normal, bradypnea, and tachypnea patterns from non-apnea signals. During model building using the Apnea-ECG dataset, the proposed hyper-feature algorithm for apnea detection demonstrates excellent performance, achieving accuracies of 92.33%, 94.89%, and 97.66% for Chest, Abdominal, and Nasal respiration signals, respectively. With an average inference time of 5 ms for respiratory event detection, the classifier achieves outstanding accuracy rates of 91.24%, 92.42%, and 93.26% for these signals on the validation dataset after being implemented and tested at the edge device. Real-time data analysis from 10 subjects further underscores the system's potential for continuous respiratory and cardiac monitoring in real-world scenarios.